We propose a novel approach to the estimation of multiple Graphical Models to analyse temporal patterns of association among a set of metabolites over different groups of patients. Our motivating application is the Southall And Brent REvisited (SABRE) study, a tri-ethnic cohort study conducted in the UK. We are interested in identifying potential ethnic differences in metabolite levels and associations as well as their evolution over time, with the aim of gaining a better understanding of different risk of cardio-metabolic disorders across ethnicities. Within a Bayesian framework, we employ a nodewise regression approach to infer the structure of the graphs, borrowing information across time as well as across ethnicities. The response variables of interest are metabolite levels measured at two time points and for two ethnic groups, Europeans and South-Asians. We use nodewise regression to estimate the high-dimensional precision matrices of the metabolites, imposing sparsity on the regression coefficients through the dynamic horseshoe prior, thus favouring sparser graphs. We provide the code to fit the proposed model using the software Stan, which performs posterior inference using Hamiltonian Monte Carlo sampling, as well as a detailed description of a block Gibbs sampling scheme.
翻译:我们建议一种新颖的方法来估计多种图形模型,以分析一组代谢物之间对不同患者群体联系的时间模式。我们的激励性应用是南和布伦特Revisited(SABRE)研究,这是在英国进行的一项三民族群体研究。我们有兴趣查明代谢物水平和协会以及它们随着时间的推移的演变中潜在的种族差异,目的是更好地了解不同族裔间心血管-代谢紊乱的不同风险。在巴耶西亚框架内,我们采用一种不偏颇的回归方法来推断图表的结构,在时间和种族间相互借用信息。兴趣的响应变量是在两个时间点测量的代谢物水平,针对两个族裔群体,即欧洲人和南亚人。我们用不偏颇的回归法来估计代谢物的高维度精确基座,通过先动的马蹄对回归系数施加过敏性,从而偏向稀释式图。我们提供代码,以Stan软件来匹配拟议的模型,该软件使用汉密尔顿-蒙特-卡洛取样模型来进行详细的图像取样。